In a dual energy CT imaging, the Hounsfield unit (HU) of non-water materials may have different values depending on the size and composition of the scanned object due to beam-hardening effects. As a consequence, the location of specific materials like Iodine contrast on the dual energy HU-HU plane may vary and depends on the composition and size of the patient. This phenomenon may degrade the material classification and quantification within the patient. The following two approaches have been proposed to reduce such degradation.
“Material separation with dual layer CT,” R. Carmi, G. Naveh and A. Altman, IEEE Nuclear Science Symposium records (2005), proposes correcting, in the image domain, on the bases of calculating, for each image pixel, a mean beam hardening factor. The instability mentioned above is sensitively dependent on the interplay between the beam hardening and the energy attenuation profile of the material under consideration, e.g. Iodine. This image domain approach does not take into account this interplay, and, therefore, unfortunately, suffers from a limited accuracy and robustness.
R. E. Alvarez and A. Macovski, Phys. Med. Biol. 21, 733 (1976), proposes a two-base model that includes decomposing the attenuation coefficient within the projection domain into two components resulting from scatter and photoelectric effect absorption. According to this model, the attenuation energy dependent profile of different materials is represented as a linear combination of scatter and photoelectric effect profiles united for all the materials. Unfortunately, the limited accuracy of this approximation, combined with beam hardening, limits the accuracy and the robustness of this approach.
FIGS. 1, 2 and 3 illustrate how the limited accuracy of the latter approach may lead to beam hardening distortions. For this example, two simulated phantoms, a liver phantom 102 (FIG. 1) and a liver and a thorax phantom 202 (FIG. 2), include the same concentration of Iodine 104 and 204 in liver organs 106 and 206. A single slice circular scan of these phantoms was simulated for 80 and 140 kVp, followed by a water-based correction and a projection domain decomposition for the water scatter and photoelectric effect components. Note that the results of the simulations mimic or parallel the results that would be obtained by performing an actual scan.
FIG. 3 shows the resulting locations of Iodine solutions for the two different materials on a dual energy scatter/photoelectric effect HU-HU plane 300. In FIG. 3, a y-axis 302 represents scatter HU and an x-axis 304 represents photoelectric effect HU. The approximation embedded, when applying the two base model to Iodine, which has a K-edge within the relevant energy range, has limited accuracy. Due to this limited accuracy, combined with beam hardening distortions, Iodine points 306 and 308, although the same concentration, are located at different (x, y) coordinates for the two different phantoms 102 and 202. In view of at least the above, there is an unresolved need for other approaches for processing multi-energy imaging data which mitigate such beam hardening distortions.